Image Processing - Syllabus

Embark on a profound academic exploration as you delve into the Image Processing course (IP) within the distinguished Tribhuvan university's CSIT department. Aligned with the 2074 Syllabus, this course (CSC321) seamlessly merges theoretical frameworks with practical sessions, ensuring a comprehensive understanding of the subject. Rigorous assessment based on a 60 + 20 + 20 marks system, coupled with a challenging passing threshold of , propels students to strive for excellence, fostering a deeper grasp of the course content.

This 3 credit-hour journey unfolds as a holistic learning experience, bridging theory and application. Beyond theoretical comprehension, students actively engage in practical sessions, acquiring valuable skills for real-world scenarios. Immerse yourself in this well-structured course, where each element, from the course description to interactive sessions, is meticulously crafted to shape a well-rounded and insightful academic experience.

Course Description: This course covers the investigation, creation and manipulation of digital

images by computer. The course consists of theoretical material introducing the mathematics

of images and imaging. Topics include representation of two-dimensional data, time and

frequency domain representations, filtering and enhancement, the Fourier transform,

convolution, interpolation. The student will become familiar with Image Enhancement, Image

Restoration, Image Compression, Morphological Image Processing, Image Segmentation,

Representation and Description, and Object Recognition.

Course Objectives: The objective of this course is to make students able to:

Ø develop a theoretical foundation of Digital Image Processing concepts.

Ø provide mathematical foundations for digital manipulation of images; image

acquisition; preprocessing; segmentation; Fourier domain processing; and compression.

Ø gain experience and practical techniques to write programs for digital manipulation of

images; image acquisition; pre-processing; segmentation; Fourier domain processing;

and compression.



Definition of digital image, pixels, representation of digital image in spatial domain as well as in matrix form.Block diagram of fundamentals steps in digital image processing, application of digital image processing system, Elements of Digital Image, Processing systems,Structure of the Human, Image Formation in the Eye, Brightness Adaptation and Discrimination, Basic Concepts in Sampling and Quantization,Representing Digital Images, Spatial and Gray- Level Resolution, Neighbors of a Pixel, Adjacency, Connectivity,

Regions, and Boundaries, Distance Measures

between pixels

Image Enhancement and Filter in Spatial Domain

Point operations, Contrast stretching, clipping and thresholding, digital negative, intensity level slicing, log transformation, power log transformation, bit plane slicing, Unnormalized and Normalized Histogram, Histogram Equalization, Use of Histogram Statistics for Image Enhancement, Basics of Spatial Filtering, Linear filters, Spatial Low pass smoothing filters, Averaging, Weighted Averaging, Non-Linear filters, Median filter, Maximum and Minimum filters, High pass sharpening filters, High boost filter, high frequency emphasis filter, Gradient based filters, Robert Cross Gradient Operators, Prewitt filters, Sobel filters, Second Derivative filters, Laplacian filters, Magnification by replication and interpolation

Introduction to Morphological Image Processing

Logic Operations involving binary images, Introduction to Morphological Image Processing, Definition of Fit and Hit, Dilation and Erosion, Opening and Closing

Image Segmentation

Definition, Similarity and Discontinuity based techniques, Point Detection, Line Detection, Edge Detection using Gradient and Laplacian Filters, Mexican Hat Filters, Edge Linking and Boundary Detection, Hough Transform, Thresholding: Global, Local and Adaptive Region Based Segmentation: Region Growing Algorithm, Region Split and Merge Algorithm

Representations, Description and Recognition

Introduction to some descriptors: Chain codes, Signatures, Shape Numbers, Fourier Descriptors, Patterns and pattern classes, Decision-Theoretic Methods, Introduction to Neural Networks and Neural Network based Image Recognition, Overview of Pattern Recognition with block diagram

Lab works

Laboratory Works:

Students are required to develop programs in related topics using suitable programming

languages such as MatLab or Python or other similar programming languages.